The present invention relates to a wide-band speech spectral quantizer and, more particularly, to improvements in spectral quantizers therein.
Prior art of such coders are disclosed in R. D. Jacovo et. al., "Some experiments of 7 kHz audio coding at 16 kbit/s", IEEE Proceeding of ICASSP, 1989, PP. 192-195 (Literature 1), M. Yong, "Subband vector excitation coding with adaptive bit-allocation", especially on pages 743-746 in S14.3, IEEE Proceeding of ICASSP, 1989 (Literature 2), V. Cuperman and A. Gersho, "Vector Predictive Coding of Speech at 16 kbit/s", July 1985, COM-33, No. 7, pp.685-696 (Literature 3), and A. Gersho and R. M. Gray, "Vector Quantization and Signal Compression", Kluwer Academic Publishers, 1992, pp. 487-517 (Literature 4).
In the prior art wide-band speech quantizers in wide-band speech coders described in Literatures 1 and 2, an input speech signal is divided or cut out into frames with a predetermined time interval, and each frame speech signal is frequency band split (or band split as hereinafter referred to). Then, spectral coefficients of each sub-band speech signal are obtained through analysis thereof and then quantized.
The spectral coefficient quantization performance is improved by methods described in Literatures 3 and 4. In these methods, spectral coefficients of the present frame are linearly predicted by using quantized spectral coefficients which were transmitted in past frames, and its prediction error is quantized.
The two methods noted above may be readily combined for use. A quantizer in which the two methods are combined, is referred to as a prior art wide-band speech spectral coefficient quantizer. In this prior art system, an input speech signal is first band-splitted, and spectral coefficients of each sub-band speech signal, which are obtained through analysis of the same sub-band speech signal, is used to linearly predict its error by inter-frame prediction, and the prediction error is quantized. Examples of this prior art system will now be described with reference to FIGS. 10 and 11.
FIG. 10 shows a first example of the prior art wide-band spectral coefficient quantizer. A frame circuit 2 cuts out frames with a predetermined window length (of 20 ms. for instance) from a speech signal inputted from an input terminal 1. A band splitter 3 band splits each frame (for instance into three sub-bands of 0 to 2, 2 to 4, and 4 to 8 kHz by sampling at 16 kH), and computes each sub-band speech signal. Analyzers 5 and 7 each computes spectral coefficients of each sub-band speech signal through analysis thereof. Each spectral coefficient usually consists of a plurality of different values. Thus, the spectral coefficients are hereinafter considered as a vector. Adders 15 and 17 each obtains a prediction error vector e(i) by subtracting a predicted spectral coefficient vector s.sub.-- (i) computed in each of optimum prediction circuits 11 and 14 from a spectral coefficient vector s(i) outputted from each of the analyzers 5 and 7. Quantizers 20 and 24 obtain a quantized prediction error vector e.sub.-- (i) by quantizing the prediction error vector e(i). Adders 8 and 18 each compute a quantized spectral coefficient vector s.sub.-- (i) by adding the predicted coefficient vector s.sub.-- (i), which is computed in each of the optimum prediction circuits 11 and 14, to the quantized prediction error vector e(i). The computed quantized spectral coefficient vector s (i) is outputted from each of output terminals 21 and 22. The optimum prediction circuits 11 and 14 each compute the predicted coefficient vector s.sub.-- (i) from the quantized error vector e.sub.-- (i) received form each of the quantizers 11 and 14 and the spectral coefficient vector s(i) received from each of the analyzers 5 and 7. The prediction is executed for N past frames.
In the band splitter 3, the band division may be executed by a method using a Quadrature Mirror Filter (hereinafter referred to as QMF). The QMF is detailed in D. Estevan and C. Galand, "Application of Mirror Filters to Split Band Voice Coding Schemes", IEEE Proceeding of ICASSP, pp. 191-195, 1977 (Literature 5).
In the analyzers 5 and 7, the LPC analysis may be executed by means of autocorrelation analysis, covariance analysis, etc.
FIGS. 3 and 4 show examples of realizing the optimum prediction circuits 11 and 14. In the example shown in FIG. 3, Auto-Regressive (AR) prediction is executed. In the example shown in FIG. 4, Moving-Average (MA) prediction is executed.
Where the optimum prediction circuit shown in FIG. 3 is used, the adder 15 computes the quantized spectral coefficient vector s (i) of the spectral coefficient from a past quantized prediction error vector e.sub.-- (i) inputted from an input terminal 25 and the predicted spectral coefficient vector s.sub.-- (i) by using an equation: EQU s (i)=e.sub.-- (i)+s.sub.-- (i)
A buffer 14 stores quantized prediction error vectors for N past frames, N being referred to as inter-frame prediction order. A gain computer 33 receives the spectral coefficient vector s(i) from an input terminal 23 and the past spectral coefficient vectors s.sub.-- (i-1), . . . , s.sub.-- (1-N) from the buffer 1, and computes prediction errors .alpha.(1), . . . , .alpha.(N) by solving a matrix equation: ##EQU1## where the vectors are all longitudinal vectors, and "T" in each vector term represents transposition of vector. A gain quantizer 35 quantizes the computed prediction errors .alpha.(1), . . . , a(N). In this case, it is efficient to vector-quantize each gain. A prediction circuit 37 receives the quantized prediction errors .alpha. (1), . . . , .alpha. (N) from the gain quantizer 35 and the predicted spectral coefficient vectors s.sub.-- (i-1), . . . , s.sub.-- (i-N) stored in the buffer 14, and computes the predicted spectral coefficient vector s.sub.-- (i) by using the following equation, the computed predicted spectral coefficient vector s.sub.-- (i) being outputted from an output terminal 21. EQU s.sub.-- (i)=.alpha.(1)s.sub.-- (i-1)+. . . +.alpha.(N)s.sub.-- (i-N)
The example shown in FIG. 4 is the same as the example shown in FIG. 3 except for that it does not use the adder 15. In this example, the buffer 14 thus receives the quantized prediction error vector e.sub.-- (i) instead of the predicted spectral coefficient vector s.sub.-- (i) given by equation (1). For the remainder, the processing in this example is the same as in the example shown in FIG. 3.
In the quantizers 20 and 24, the spectral coefficient quantization may be executed by using LPC coefficients as spectral coefficients. Specifically, in this method the LPC coefficient are converted into linear spectrum pair (LSP) coefficients, which are then vector quantized. Vector quantization of LSP coefficients are treated in, for instance, K. K. Paliwal and Bishnu and S. Atal, "Efficient Vector Quantization of LPC Coefficients at 24 Bits/Frame", IEEE Trans. on Speech and Audio Processing, Vol. 1, No. 1, pp. 3-14, January 1993 (Literature 7).
FIG. 11 shows a second example of the prior art wide-band speech quantizer. In the first example, the computations are executed for each frame, and the inter-frame prediction is executed by using the quantized prediction errors. In the second example, as shown in FIG. 11, fixed prediction circuits 12 and 16 each compute the predicted spectral coefficient vector s.sub.-- (i) through inter-frame prediction by using the quantized prediction error vector e.sub.-- (i) received from each of the quantizers 20 and 24 and a predetermined fixed prediction error. The first and second examples are different from each other only in the prediction circuit part, and the remainder of the construction is not described in detail. In the second example, deterioration of the prediction performance is anticipated, but on the merit side it is possible to reduce data to be transmitted for the prediction error quantization.
FIGS. 5 and 6 show examples of realizing the fixed prediction circuits 12 and 16 shown in FIG. 11. In the example shown in FIG. 6, MA prediction is executed.
The fixed prediction circuit shown in FIG. 6 and the optimum prediction circuit shown in FIG. 4 are different from each other in that the former circuit uses prediction errors stored in a gain table circuit 51, whereas the latter circuit uses prediction errors that are computed in a gain computer 33. The fixed prediction circuit shown in FIG. 5 and the optimum prediction circuit shown in FIG. 3 are different from each other likewise.
In the above prior art wide-band speech quantizers, however, the spectral coefficient quantization is executed without taking the correlationship among changes in sub-band spectral coefficients with time into considerations. This is so because the inter-frame prediction is executed independently in each sub-band.